摘要
提出了一种利用稀疏表达检测多幅图像中协同显著目标的方法。首先用独立变量分析方法训练得到自然图像一组稀疏基,接着求出检测图像的稀疏表达,然后定义了多变量K-L散度度量它们之间的相似性,最后,根据K-L散度性质找出散度下降明显的地方,检测出多幅图像的共同显著性目标。实验结果表明,该方法正确有效,具有和人类视觉特性相符合的显著性目标检测效果。
We propose what we believe to be a new algorithm for detecting the co-saliency in muhiple images. First, we use the independent component analysis to learn and obtain a set of sparse bases of a natural image through filtering the input image and then use them to work out the sparse coding representation of the image to be detected. Second, we define the multi-variable Kullback-Leibler （K-L） divergence to measure the similarity among multiple images. Third, according to the properties of the K-L divergence, we detect the region where the divergence decreases significantly, or the similarity of the image, thus detecting the co-saliency in multiple images. To verify the effectiveness of our algorithm, we test the image co-saliency detection effect with the photos we took. The test results, given in Fig. 3, and their analysis show preliminarily that the image co-saliency detection effect of our new algorithm is the same as that of human visual characteristics.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2013年第2期206-209,共4页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61273362)
西北工业大学基础研究基金(NPU-FFR-JC201041)资助
关键词
算法
图像处理
独立变量分析
协同显著性
稀疏表达
K—L散度
algorithm, image processing, independent component analysis
co-saliency, sparse coding representation, Kullback-Leibler divergence
